Distinguishing neighborhood and individual social risk factors in health care. (20th October 2021) - Beckett, Megan K.; Martino, Steven C.; Agniel, Denis; Mathews, Megan; Hudson Scholle, Sarah; James, Cara; Wilson‐Frederick, Shondelle; Orr, Nate; Darabidian, Biayna; Elliott, Marc N.
Abstract: Objective: To investigate (a) the magnitude of the independent associations of neighborhood‐level and person‐level social risk factors (SRFs) with quality, (b) whether neighborhood‐level SRF associations may be proxies for person‐level SRF associations, and (c) how the association of person‐level SRFs and quality varies by neighborhood‐level SRFs. Data sources: 2015–2016 Medicare Advantage HEDIS data, Medicare beneficiary administrative data, and 2016 American Community Survey (ACS). Study design: Mixed effects linear regression models (1) estimated overall inequities by neighborhood‐level and person‐level SRFs, (2) compared neighborhood‐level associations to person‐level associations, and (3) tested the interactions of person‐level SRFs with corresponding neighborhood‐level SRFs. Data collection/extraction methods: Beneficiary‐level SES and disability administrative data and five‐year ACS neighborhood‐level SRF information were each linked to HEDIS data. Principal findings: For all or nearly all HEDIS measures, quality was worse in neighborhoods lower in SES and in neighborhoods with higher proportions of residents with a disability. Quality by neighborhood racial and ethnic composition was mixed. Accounting for corresponding person‐level SRFs reduced neighborhood SRF associations by 25% for disability, 43% for SES, and 74%–102% for racial and ethnic groups. Person‐level SRF coefficients were not consistently reduced in models that added neighborhood‐level SRFs. In 19 of 35 instances, there were significant ( p  < 0.05) interactions between neighborhood‐level and corresponding person‐level SRFs. Significant interactions were always positive for disability, SES, Black, and Hispanic, indicating more negative neighborhood effects for people with SRFs that did not match their neighborhood and more positive neighborhood effects for people with SRFs that matched their neighborhood. Conclusions: Relying solely on neighborhood‐level SRF models that omit similar person‐level SRFs overattributes inequities to neighborhood characteristics. Neighborhood‐level characteristics account for much less variation in these measures' scores than similar person‐level SRFs. Inequity‐reduction programs may be most effective when targeting neighborhoods with a high proportion of people with a given SRF.